Solmondxg/crop-yield-prediction

Uganda Crop Yield Prediction - ML project for predicting maize yield using climate and soil data # Uganda Crop Yield Prediction A machine learning project that predicts maize yield in Uganda using agricultural and climate data. ## 📋 Project Overview This project demonstrates an end-to-end machine learning workflow to predict crop yield based on climate and soil conditions. The goal is to help farmers and policymakers in Uganda make data-driven decisions to improve agricultural productivity and food security. ## 🎯 Problem Statement Agriculture is the backbone of Uganda's economy, but several challenges exist: - **Climate Unpredictability**: Weather patterns are becoming increasingly unpredictable - **Farmer Decision-Making**: Farmers lack data-driven insights for better planning - **Food Security**: Population growth requires improved crop productivity - **Resource Optimization**: Limited resources need to be allocated efficiently **Solution**: Use machine learning to predict crop yield based on historical patterns and environmental factors. ## 📊 Dataset ### Source - Kaggle: Search "crop yield prediction", "maize yield Uganda" - FAO (Food and Agriculture Organization): agro-climatic and yield statistics ### Features Used | Feature | Type | Description | |---------|------|-------------| | Rainfall_mm | Numerical | Annual/seasonal rainfall in millimeters | | Temperature_C | Numerical | Average temperature in Celsius | | Soil_Type | Categorical | Type of soil (Sandy, Loam, Clay) | | Season | Categorical | Growing season (Wet, Dry) | | Year | Numerical | Year of observation | | District | Categorical | Region in Uganda | | Fertilizer_kg_ha | Numerical | Fertilizer application rate | | **Yield_tons_ha** | **Numerical** | **Crop yield (TARGET VARIABLE)** | ### Dataset Statistics - **Size**: 200+ records - **Time Period**: 2015-2025 - **Regions**: Multiple districts across Uganda - **Classes**: Balanced across different soil types and seasons ## 🔧 Methodology ### 1. Exploratory Data Analysis (EDA) - Analyzed distributions of numerical features - Identified …